To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.

Lung cancer is the leading cause of cancer-related deaths worldwide, and imaging techniques such as chest radiography, computed tomography (CT), positron emission tomography (PET), and magnetic resonance imaging (MRI) play an important role in its diagnosis, staging, treatment planning, post-operative surveillance, and treatment response evaluation. Pulmonary MRI can non-invasively visualize structural and functional abnormalities in the lungs without using ionizing radiation, although it has been suggested that it has less clinical utility than chest radiography, CT, and PET/CT for thoracic diseases, especially lung diseases. With recent advances related to MRI pulse sequences, pulmonary MRI has become practicable in an expanding number of clinical situations. This review article focuses on recent advances in MRI and discusses its clinical applications in the detection, diagnosis, staging, pre-operative evaluation, post-operative surveillance, and treatment response evaluation of lung cancer.

This study investigated the rate at which radiologists miss or detect incidental breast cancers on chest CT and to compare the CT features between the two groups. This retrospective study evaluated chest CT examinations and medical records of patients who registered with the diagnosis code of “breast cancer” between January 2016 and December 2020, and who had undergone contrast enhanced chest CT 3–18 months before registration, during which they were unaware of any breast lesions. This study found that out of 84 patients, incidental breast cancer lesions were missed in 54 (64.3%) and detected in 30 (53.7%). The initial treatment was delayed in the missed breast lesions group (p = 0.004). Breast lesions of smaller sizes (<9.0 mm, p = 0.01), or with lower enhancement ratios (<1.4, p = 0.009), were more likely to be missed. When three radiologists re-read the CTs with more attention to breast area, they detected breast cancers with higher accuracies (90.1%, 87.9%, and 81.3%). In summary, this study revealed that radiologists miss 64.3% of incidental breast cancers on chest CT, especially those of sub-centimeter sizes and weak enhancements.

Background: In the process of video-assisted thoracoscopic surgery (VATS) for lung nodule resection, lung is leaded to atelectasis. However, preoperative computed tomography (CT) images are taken during inspiration, which means they differ significantly from the lung status observed during surgery. Consequently, this discrepancy can make the localization of small or subsolid nodules challenging during the operation. This study aimed to develop a CT-based virtual atelectasis simulation system for noninvasive lung nodule localization. By accurately simulating atelectasis, this study aimed to improve the precision of presurgical planning from lung nodule resections. Methods: This study retrospectively examined 20 patients who had either subsolid nodules or small nodules less than 3 cm in size, selected from a cohort of 279 patients who underwent VATS surgery for lung nodules in Korea University Ansan Hospital between June 28, 2021, and January 22, 2024. Chest CT images of the lungs of 20 patients were acquired, and image data were converted three-dimensional models. The mesh points extracted from these lung models were manipulated to simulate the effects of gravity, by adjusting the lung shapes and nodule locations to align with the respective surgical postures of the patients. Subsequently, we assessed the similarity of the simulation by comparing the resulting deformed lung shapes and nodule locations with the corresponding perspectives observed in the surgical videos. Results: The average volume of the entire lung among the patients was 2,336 cm³ (±588). After atelectasis simulation, the average lung shrinkage rate was 48.6% (±12.9%). Evaluations of an average of 15 pairs of images per case revealed significant conformity between atelectasis simulation images and surgical video snapshots, with average Dice and Jaccard similarity coefficient values of 90.27 and 88.25, respectively. Furthermore, the alignment of nodule locations between the simulations and surgical anticipation demonstrated notable accuracy, with an average Hausdorff distance of 6.39 mm. Conclusions: We successfully developed a simulation of lung atelectasis based on preoperative CT scans that closely resembled actual surgical videos. The integration of this presurgical atelectasis simulation is anticipated to enhance the accuracy of nodule locations, thus contributing to more efficient and precise surgical planning.

Kim C, et al. J Korean Med Sci. 2024 Jul;39:e228. https://doi.org/10.3346/jkms.2024.39.e228

Purpose: This study aimed to evaluate the predictive value of preoperative chest computed tomography (CT) for axillary lymph node (ALN) metastasis in patients with breast cancer.Methods: CT features of ALNs were retrospectively reviewed in 212 patients with breast cancer who underwent preoperative chest CT examination and ALN dissection. Primary tumor size and CT characteristics of ALNs (cortical thickness, cortical shape, the presence or absence of contrast enhancement of ALNs, and the presence or absence of perinodal infiltration) were recorded and analyzed. A nomogram was developed to visualize the associations between the predictors and each ALN status endpoint.Results: Of 212 patients, 95 (44.8%) had ALN metastasis. Primary tumor size and CT characteristics of ALNs were identified as predictors of ALN metastasis. The nomogram comprising primary tumor size and cortical shape was a good validation model for predicting ALN metastasis. The sensitivity, specificity, and accuracy of the nomogram for predicting ALN metastasis were 88.4%, 79.5%, and 83.5%, respectively.Conclusion: Using preoperative chest CT scans, a nomogram combining the cortical shape of ALNs with the primary tumor size showed good performance in predicting ALN metastasis.